Top 13 Business Analytics Projects To Enhance Your Resume & Portfolio


The fascinating world of data and AI has brought forth many scientific tools, algorithms, processes, and knowledge extraction systems to identify meaningful patterns from structured as well as unstructured data. The boom in data analytics in the last couple of years is only growing and will reach the next level with so many innovations in the artificial intelligence domain.

If Data Analytics is something you fancy and want to get a solid foundation on this topic, then you must have a portfolio of data analytics projects to showcase. If you are wondering how to start with data analytics, we have here data analytics project ideas that are good for beginners as well as those who are in intermediate or higher levels. If you are a student, then our ideas could also be used for data analytics projects for students.

Why Data Analytics Projects? 

Data Analytics projects are the best way for aspiring Data Science professionals to gain hands-on experience. In these projects, you will get to deploy various Data Science and Machine Learning algorithms in real-world scenarios to uncover connections between data points and understand how different variables may impact each other. The more you practice on Data Analytics projects, the stronger you build your portfolio to showcase your expertise to potential employers. 

Tips To Include Data Analytics Projects To Enhance Your Resume 

Showcasing all the Data Analytics projects you’ve worked on can help you create a resume that distinguishes you from other candidates with similar job experiences and academic credentials. Below are some tips for including Data Analytics projects on your resume.  

  • Adjust As Per The Job Description: Go through the job description keenly, identify what skills the recruiting manager is looking for, and then choose relevant projects that demonstrate your abilities in those areas. 
  • Highlight Under A Separate Project Section: If you have worked on a diverse range of projects, try including a separate projects section on your resume to showcase them. Alternatively, you can try including projects in the job experience or education sections. 
  • Adding A Link To Your Portfolio: You might provide a link to your portfolio and your contact information to urge hiring managers to look into other projects you’ve worked on.  

Data Analytics Projects (Easy, Medium, Hard)

To get started with data analytics project topics, you would first need to understand what level you are comfortable in and then decide whether you want to get on with data analytics projects for beginners, intermediate, or higher levels. Let us take a look at what it entails to do a project in these 3 levels:

  • Beginner level – If you are someone who is just starting with data analytics, you must go through the data analytics project examples in the beginner section. These projects do not employ heavy application techniques, and their simple algorithms would let you move forward smoothly.
  • Intermediate – Here, medium to large data clusters are taken and need you to have a sound foundation of data mining projects along with machine learning techniques. If this is something you are well-versed with, then you can work on the projects outlined in the intermediate section.
  • Expert – This section is for industry experts where neural networks and high-dimensional data are worked with. If you have the blend of creativity and expertise required for such projects, then the data analytics mini project in the advanced section is for you.
  1. Easy or Beginner level projects
  2. Intermediate Level Projects
  3. Advanced Level Project

Easy or Beginner level projects

  1. Fake News Detection If you know python, then you could develop this data analytics project in python which can detect a hoax or false news that is generated to fulfill some political agenda. This news is propagated through social media channels and other online media. The model is built using the python language, which can accurately detect the genuineness of a news item. You could use a PassiveAggressiveClassifier to build a TfidfVectorizer, which can classify news into “fake” or “real.”
  2. EDA or Exploratory Data Analysis Project – This is the first thing a data analyst needs to do as part of their job. In this project, we look into data to recognize and identify patterns. Using data modeling techniques, you can summarize the overall features of data analysis. EDA could be done with or without the help of graphics. You could also use univariate or bivariate quantities to perform EDA. The IBM Analytics community is valuable if you want to delve into an EDA project.
  3. Sentiment Analysis – This analysis is used widely in online communities for brand reputation management or to perform competitor analysis using the R framework. This data analytics project in r will try to understand the opinions and sentiments of viewers based on the words they use. In this classification, classes are either binary (positive or negative) or multiple (happy, angry, sad, confused, disgusted, etc.). You could use the “Jane Austen” package with a relevant dataset. Using general-purpose lexicons like bing, Loughran, and AFINN and performing an inner join, you could build a word cloud for the final display of the data analytics project report.
  4. Colour Detection Project – This is a good data analytics project for students where they can build an interactive app to detect the selected color from an image. Many of us can not recognize or remember the name of color since there can be around 16 million colors based on RGB values.
  5. Forecasting Sales for a New Car Design – This project requires a thorough examination of consumer needs and desires. You can work on a project to see if a new automotive design, color, or form will appeal to the target demographic. There are numerous autos on the market to assist you in determining the most popular vehicle.
  6. Predicting a Product’s Success – In this project, you will use your analytical skills to analyze whether a specific product will sell well in a given market. You may, for example, concentrate on the entertainment industry. With thousands of hours of content distributed daily, determining which music or movie will do well is difficult. To make forecasts, you will need to leverage previous data and models.
  7. Insights From Employee Performance and Resignation Statistics – In this assignment, you will provide statistics to a corporation that can explain why employees depart. The intent is to use these insights to improve the company environment. You can consider the employee’s proximity to home, work culture, or job description. It would be best to weigh each element concerning the likelihood of resignation.

Intermediate Level Projects

  1. Chatbots – Chatbots are an extremely useful tool in businesses as the huge surge of customer queries and messages can be handled by chatbots without slowing down business. Artificial Intelligence, Data Science, and Machine Learning are the three pillars of designing a chatbot. Chatbots can be trained using recurrent neural networks and intent JSON datasets. The main implementation could be done in python.
  2. Handwritten digit recognition – Machine learning enthusiasts widely use the MNIST datasets of handwritten digits. You use convolutional neural networks and do the real-time prediction of digits drawn on a graphical user interface.
  3. Gender and Age detection – You can build this interesting data analytics project in python which can predict gender and age by analyzing just one image. To do this project, you would need to know about computer vision and its principles.

Advanced Level Project

  1. Movie recommendation system – The concept of recommending movies is complex and is based on the abstract click method. It requires a huge implementation of machine learning and accessing humungous datasets that include users’ movie browsing history, preferences, etc. You would need to use collaborative filtering to get a hang of user’s behavior and the R Framework, along with the MovieLens dataset, is a good fit for such projects. To channel through the datasets, you could use surprise model selection and matrix factorization too. Brands like NetFlix use this method, and is a lot of grueling work even for industry experts.
  2. Credit Card Fraud Detection – Another data analytics project in r will need you to work with decision trees, gradient boosting classifiers, logistic regression, and artificial neural networks. By using the card transactions dataset, you can classify transactions on a credit card into fraudulent or genuine categories.
  3. Customer Segmentation – This is one of the most popular data analytics projects for companies as they need to create various groups of customers at the beginning of any of their campaigns. This project is an implementation of unsupervised learning and uses clustering to identify different segments of customers so that companies can target the customer base they need to. Customers are divided into groups based on age, gender, preferences, spending habits, etc. This is done to market to each group more effectively. You can use K-means clustering and visualize gender and age distributions.


We know that finding a perfect idea for your Data Analytics project could be more daunting than actually working on the project. We hope the above-mentioned Data Analytics Project ideas will be just the inspiration you’re looking for. The bottom line is that Data Science has high growth potential and continues to increase, promising in-demand opportunities for people proficient in the subject. Including projects on your resume is a definite way to make it stand out. 

Finding the right place to learn and become proficient in all these skills and languages is also important. UNext, recognized as one of the Top 10 Data Science Institutes in India, is the right place for you. UNext in collaboration with IIM Indore, offers the Integrated Program In Business Analytics for enthusiasts in this field. The course runs for 10-month and is conducted live online to aid interested learners in mastering the tricks and trades of the domain.

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